snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -202,8 +202,7 @@ class SRCNN:
             f = files[k]
             nda = np.load(f)
             data_s.append(nda)
-
-        data = np.concatenate(data_s)
+        input_data = np.concatenate(data_s)
 
         add_noise = None
         noise_scale = None
@@ -214,15 +213,15 @@ class SRCNN:
         data_norm = []
         for param in data_params:
             idx = params.index(param)
-            tmp = data[:, idx, 3:131:2, 3:131:2]
+            tmp = input_data[:, idx, 3:131:2, 3:131:2]
             tmp = resample(y_64, x_64, tmp, s, t)
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             data_norm.append(tmp)
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
 
-        # label = data[:, label_idx, 3:131:2, 3:131:2]
-        label = data[:, label_idx, 3:131, 3:131]
+        # label = input_data[:, label_idx, 3:131:2, 3:131:2]
+        label = input_data[:, label_idx, 3:131, 3:131]
         label = np.expand_dims(label, axis=3)
         if label_param != 'cloud_fraction':
             label = normalize(label, label_param, mean_std_dct)